Digital assistants have become ubiquitous in e-commerce applications, following the recent advancements in Information Retrieval (IR), Natural Language Processing (NLP) and Generative Artificial Intelligence (AI). However, customers are often unsure or unaware of how to effectively converse with these assistants to meet their shopping needs. In this work, we emphasize the importance of providing customers a fast, easy to use, and natural way to interact with conversational shopping assistants. We propose a framework that employs Large Language Models (LLMs) to automatically generate contextual, useful, answerable, fluent and diverse questions about products, via in-context learning and supervised fine-tuning. Recommending these questions to customers as helpful suggestions or hints to both start and continue a conversation can result in a smoother and faster shopping experience with reduced conversation overhead and friction. We perform extensive offline evaluations, and discuss in detail about potential customer impact, and the type, length and latency of our generated product questions if incorporated into a real-world shopping assistant.

翻译：数字助理已广泛应用于电子商务应用，这得益于信息检索、自然语言处理和生成式人工智能领域的最新进展。然而，顾客往往不确定或不知道如何有效地与这些助手对话以满足其购物需求。本文强调为顾客提供快速、易用且自然的方式与购物助手互动的重要性。我们提出一种框架，采用大型语言模型通过上下文学习和监督微调，自动生成关于产品的上下文相关、实用、可回答、流畅且多样化的问题。将这些问题作为有用的建议或提示推荐给顾客，用于启动和延续对话，可以带来更顺畅、更快速的购物体验，减少对话开销和摩擦。我们进行了广泛的离线评估，并详细讨论了若将所生成的产品问题整合到真实购物助手中，其潜在客户影响、问题类型、长度与延迟特点。